Fuzz-iDEVS: An approach to model imprecisions in Discrete Event Simulation

Paul-Antoine Bisgambiglia, Eric Innocenti and Paul Bisgambiglia

Abstract

In this work, we present a discrete event model that incorporates the lack of precision of variables. The make use of Fuzzy Set Theory to generate the model and apply it on fire propagation study case. Our aim is to propose a generalization to the discrete event system specification formalism (DEVS) which be able to deal with imprecision in all of its elements (events, transition functions, state). Our approach is based on mathematical definitions of the Fuzzy Set Theory to represent and use imprecise information in the DEVS formalism.

Based on multiDEVS formalism, we introduce multi- PDEVS, a parallel and non-modular formalism for dis- crete event system specification. This formalism pro- vides combined advantages of PDEVS and multiDEVS approaches, such as excellent simulation capabilities for simultaneously scheduled events, and components able to influence at each other using exclusively their state transitions. We next show the soundness of the formalism by giving a construction showing that any multiPDEVS model is equivalent to a PDEVS atomic model. We then present the simulation procedure as- sociated, usually called abstract simulator. As a well- adapted formalism to express cellular automata, we fi- nally propose to compare an implementation of mul- tiPDEVS formalism with a more classical Cell-DEVS implementation through a fire spread application.

This work deals with the simulation of spatial complex systems resolved with cellular models. We compare two component-based modeling implementations, based on DSDE and Multicomponent formalisms, using a Virtual Laboratory Environment framework (VLE) that is based on the DSDE formalism.

A new way to use fuzzy inference systems in activity-based cellular modeling simulations

Paul-Antoine Bisgambiglia, Eric Innocenti and Pierre-Regis Gonsolin

Abstract:

Over the last few years, both the study and the design of IT implementations of Cellular Automata Models (CAM) have gained a renewed interest. The success of these models in the Theory of Modelling and Simulation (TMS) relies on the structural phenomenon of emergence which makes it possible to run realistic simulations, despite lacking a modeling process for real systems. Cellular Automata Models (CAMs)do not describe real systems with complex equations, they allow the complexity of real systems to emerge from simple interactions described locally from their cellular elements. In order to optimize simulations whatever the spatial dimension considered, the concept of activity is used. In this work, we introduce disturbances in propagation rules and we improve simulation rendering. We express a doubt in the expression of the cell's activity, i.e. we express the activity rule by means of an Fuzzy Inference System (FIS). We present a new way to use Fuzzy Inference System (FIS), in an activity-based cellular modeling approach for fire spreading simulations.

Published in: Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on